To determine policy premiums, auto insurers have traditionally used factors such as driving record, vehicle usage and insurance history. This is in addition to outside variables like customer location (collision/theft rates in the area), age and gender (teen males tend to have higher rates), and non-driving elements like credit score.

Recently, however, some insurance companies have begun to harness the internet of things to monitor real-time driving habits. By employing telematics devices, which plug in to the vehicle’s on-board diagnostic port, insurers are provided with analytics about a policyholder’s specific behind-the-wheel behaviors. So, instead of paying for collision coverage even when the vehicle is parked in the garage, customers would only pay for it when the car’s being driven.

This is called usage-based insurance, and Business Insider Intelligence estimates that over 50 million U.S. drivers will have tried UBI by 2020. By leveraging such refined data, premiums can be closely tailored around a driver’s level of risk. For safe drivers, especially, the prediction is that car insurance rates will be lowered.

But what we’ve seen with telematics is only the beginning. According to an SMA research survey, 74% of insurance executives believe IoT will disrupt the industry by 2020. It’s therefore no surprise that many insurers have begun to invest in IoT research and implementation. When autonomous cars hit our roads en masse, the business model and underwriting practices of auto insurance companies are likely to be changed forever.

Embracing IoT to evaluate claims

The National Insurance Crime Bureau reports that customers shell out an extra $200 to $300 per year on premiums to counteract the cost of fraudulent claims. While top-tier insurers do rely on rigorous investigative methods and analytics, the insurance industry still estimates that at least 10% of property-casualty claims could be fraudulent.

However, the UBI model already exhibits promise as a fraud-detection system. For instance, a common form of fraud is exaggerating vehicle damage after, say, a minor fender-bender in the hopes of getting a higher claim payout. But by cross-referencing that claim with real-world data showing the time, speed, location and position of the vehicles in an accident, insurers can better gauge how its severity stacks up against the nature of the claim.

And when vehicles are regularly communicating with each other and highway infrastructure, the rich tapestry of information therefrom can be leveraged for even more predictive fraud exposure analytics, thus, saving consumers hundreds of dollars every year.

How IoT is changing the nature of risks

Insurance policies are largely designed to financially safeguard drivers in the event human error causes an accident. But IoT is expected to significantly minimize that risk.

Tesla’s autopilot feature, for instance, has already led to a 40% reduction in crashes — the success of which is the result of abating human error. Additionally, a report by U.S. consulting firm McKinsey & Company suggests driverless car technology may reduce accidents by as much as 90% by the midcentury, saving roughly $190 billion in hospital and emergency room expenses.

Plus, once the majority of deployed cars on the road are equipped with tracking systems, theft will be more effectively deterred and stolen vehicles easily recoverable.

This means a few things. For one, fewer incidents of collision and theft in a given region could contribute to lower overall rates since, as mentioned earlier, both can impact premiums for all consumers who live there. Secondly, autonomous technology can potentially mitigate the likelihood of an otherwise high-risk driver. As more drivers have better driving histories, a greater number of consumers will have easier access to standard — and not exorbitantly priced — auto insurance.

As common insurable risks become preventable, auto insurance companies will need to update their business model. Whereas insurers have been traditionally tasked with policy renewals, updates and claims-handling following an accident, they may need to refocus their efforts on evolving safety and loss-avoidance platforms for customers.

IoT and cybersecurity risks

It’s slated to be a game-changer, but IoT technology still presents its own unique risks for which new lines of insurance coverage will become absolutely crucial — namely, cybersecurity.

Recall the major cyberattack against domain name system provider Dyn back in October of 2016. Hackers were able to easily corrupt thousands of IoT-enabled cameras, printers and DVRs, assembling them in a botnet campaign to infect Dyn with malware and thereby cause it to crash. As a result, popular websites for which it routed traffic — including Netflix, Reddit, Amazon and Twitter — were rendered unavailable to a large portion of users in the U.S. and Europe.

One could say that this is a harbinger for more to come, especially as more and more things become connected to the internet. The cyberinsurance market is already focused mostly on data protection, and is forecasted to reach $14 billion by 2022 — a 28% increase from 2016.

That said, the emergence of affirmative cyberinsurance products and liability policies is only scratching the surface. Once IoT-enabled vehicles are broadcasting massive volumes of data en masse, so too will cybersecurity become an even greater insurable necessity.

IoT may deliver monumental advantages to those who begin honing it early on. But with tremendous potential afforded by mountains of data, comes complexity. Insurers will need to up their data analytics and figure out how to effectively filter out the “noise” and thus discern the “real tidbits” from the trivial or redundant. Only then can companies successfully weigh hundreds of new factors and create remarkably accurate pricing profiles for consumers.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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It is the Internet of Things, but with an enterprise angle. Take that to mean industry vertical applications, development ecosystems, product design, hardware, deployment and more.